**2. Hypotheses development**

In spite of the distinctive nature of the principles of internal controls according to the BCBS framework, they overlap and reinforce each other. For this reason, the author proposes three broad thematic areas which comprise board activities and functions, board structure and board monitoring.

#### **2.1 Board functions and activities**

The functions and activities of board of directors affect their supervisory and fiduciary role in protecting the interest of shareholders. The activities and functions of the board of directors affect managerial behavior. From the institutional theory, Zucker [27] explains that it is a complex view of the organization and how it responds to normative pressures from the internal and external environment that compels the organization to take legitimate stance to respond to such pressures. Institutional theories emphasize standard systems and procedures for the conduct of business to ensure survival of the organization. Scott [28] outlined three factors of institutionalization which comprises cognitive elements (systems and cultural foundations of society), normative elements (expectation from acceptable behavior) and enforcement processes (assessment, surveillance and sanctioning). Relating this theory to the BCBS internal controls framework, it implies drafting, implementing and improving policies that lead to acceptable behavior. It connotes a policy of creating, exemplifying and sustaining a culture of ethical behavior and compliance. The BCBS framework emphasizes enforcing sound internal control measures and this is a function of the expertise of the audit committee. The expertise of the board provides assurance for quality and efficiency in discharging board functions [29]. Board of directors carry out their activities by attending meetings and participating in committee tasks. The activities of board members are also about the number of meetings held within the financial year. We expect audit committee expertise, board policy functions and number of board meetings to significantly reduce credit risk. We therefore hypothesize that:

*H1: Board functions and activities minimize credit risk.*

#### **2.2 Board structure**

The structure of a board determines their effectiveness and efficiency with which they carry out their activities. Different studies use different variables to measure board structure. For example, Farag and Mallin [30] models board

*Internal Controls and Credit Risk in European Banking: The Basel Committee on Banking… DOI: http://dx.doi.org/10.5772/intechopen.92889*

structure in terms of unitary and dual boards and CEO duality and report no significant relation with bank fragility. Studying the UK financial sector, Akbar et al. [10] use board size, board independence and combined role of CEO and board chair as variables for board structure. The results from the UK study show that there is little evidence of CEO duality. The regression results confirm low risk taking behavior. Other authors use board size, board independence and board member affiliations as proxy for board structure [9]. The authors report that independent board structure reduces bank risks. The inconclusiveness in the findings stimulates further investigation into board structure. The structure of board of directors should ensure minimizing the agency problem through segregation of duties (as enshrined in the BCBS internal control framework). The structure, composition and characteristics of board of directors could be relevant in their oversight and control functions [31]. Board characteristics such as board composition, independence, size, and gender diversity are efficient in monitoring and control of management [32]. The authors explain that these board characteristics motivate board members in the quest to control and maintain a risk culture and sound bank management to the satisfaction of stakeholders. The current study measures board structure by nonexecutive board members, board diversity, and board chair being ex-CEO. We expect that boards with non-executive members, few cases of board chair being ex-CEO and boards with adequate female representation can demonstrate higher degree of independence. This leads to the hypothesis that:

*H2: Independent board structure reduces credit risk whilst boards with weak independence increase credit risk.*

#### **2.3 Board monitoring and control**

credit risk management strategies should therefore be comprehensive to address issues of default and prevent increasing non-performing loans. Most literature on credit risk uses ratios such as non-performing loans to total loans, provision for loan

In spite of the distinctive nature of the principles of internal controls according to the BCBS framework, they overlap and reinforce each other. For this reason, the author proposes three broad thematic areas which comprise board activities and

The functions and activities of board of directors affect their supervisory and fiduciary role in protecting the interest of shareholders. The activities and functions of the board of directors affect managerial behavior. From the institutional theory, Zucker [27] explains that it is a complex view of the organization and how it responds to normative pressures from the internal and external environment that compels the organization to take legitimate stance to respond to such pressures. Institutional theories emphasize standard systems and procedures for the conduct of business to ensure survival of the organization. Scott [28] outlined three factors of institutionalization which comprises cognitive elements (systems and cultural foundations of society), normative elements (expectation from acceptable behavior) and enforcement processes (assessment, surveillance and sanctioning). Relating this theory to the BCBS internal controls framework, it implies drafting, implementing and improving policies that lead to acceptable behavior. It connotes a policy of creating, exemplifying and sustaining a culture of ethical behavior and compliance. The BCBS framework emphasizes enforcing sound internal control measures and this is a function of the expertise of the audit committee. The expertise of the board provides assurance for quality and efficiency in discharging board functions [29]. Board of directors carry out their activities by attending meetings and participating in committee tasks. The activities of board members are also about the number of meetings held within the financial year. We expect audit committee expertise, board policy functions and number of board meetings to

losses and loan loss reserves [5, 6, 26] to measure credit risk.

significantly reduce credit risk. We therefore hypothesize that: *H1: Board functions and activities minimize credit risk.*

The structure of a board determines their effectiveness and efficiency with which they carry out their activities. Different studies use different variables to measure board structure. For example, Farag and Mallin [30] models board

functions, board structure and board monitoring.

**2. Hypotheses development**

*The credit risk trajectory. Source: Author's construct.*

**Figure 1.**

*Banking and Finance*

**2.1 Board functions and activities**

**2.2 Board structure**

**22**

Board monitoring has undergone several evolutions in corporate governance research [33]. The authors emphasize the role of the internal audit in responding to the agency problem through effective monitoring. The agency theory provides strong theoretical foundation to internal control research. The theory (traceable to the late 20th century and attributable to Jensen and Meckling) provides an underlying explanation of internal controls with the assumption that institutional behavior emanates from individual pursuit of self-interest and that there should be separation of ownership from control in order to minimize possible conflict of interest between the agent and the principal. The theory emphasizes separation of ownership from control, protection of minority interests, reducing conflict of interest and minimization of information asymmetry [34]. Jensen and Meckling [34] explain that the firm is a nexus of contracts among individual factors of production with conflicting objectives. Thus the best way of unifying these conflicts of interest is the use of contracts that minimizes the agency costs and enhances performance to maximize the value of the firm. A managerial tool put in place to check management and employee misbehavior through auditing, budgeting, compensation and other forms of control have proven successful in minimizing the agency costs [33, 35, 36]. Some high level of transparency and reporting is mandatory in order to effectively deal with information asymmetry. Internal control frameworks through the internal audit unit ensure the reporting and compliance objectives. Board audit committees reinforce the monitoring functions by ensuring compliance and adherence to internal controls [37]. Upadhyay et al. [37] conclude that board monitoring committees mitigate costs. We measure board monitoring by audit committee independence. Firms prefer using control-based approaches through audit committees with emphasis on high risk areas [38]. We propose the use of risk assessment and control by ensuring appropriate risk-weighted assets to total assets ratio to check possible insolvency. The use of risk weighted density

(ratio or risk weighted assets to total assets) has a positive relation with bank credit risk [20]. From the discussions above, two hypotheses emerge:

the set of variables on board monitoring activities and *Contr* represent the set of

*Internal Controls and Credit Risk in European Banking: The Basel Committee on Banking…*

*BodFtns* ¼ *f Audit Committee expertise* ð , *Board policy function*, *Number of board meetings*Þ

*BodStruc* ¼ *f Board diversity* ð , *Non* � *executive members*,*Chairman is ex* � *CEO*

*BodMonit* ¼ *f Audit committee independence* ð Þ , *Risk Weighted Assets to Total Assets*

*Contr* ¼ *f Capital adequacy ratio* ð Þ � *Tier* 1, *Bank profitability*, *Bank size* (5)

þ *δ*1*Audit committeee independencei*,*<sup>t</sup>* þ *δ*2*Risk weighted assets to total assetsi*,*<sup>t</sup>* þ *γ*1*Capital adequacy ratio* � *Tier* 1*<sup>i</sup>*,*<sup>t</sup>* þ *γ*2*BankSizei*,*<sup>t</sup>* þ *γ*3*Bank profitabilityi*,*<sup>t</sup>*

The chapter uses the OLS, fixed effect and random effect estimation techniques. Sometimes, the assumptions of OLS may lead to biases and misleading standard errors hence the use of fixed and random effect models. The fixed effect model assumes that certain individual characteristics within may bias the model [40]. Torres-Reyna [40] explains this as the rationale behind the correlation between the error term and predictor variables. The fixed effect model removes the effect of time-invariant characteristics thereby perfectly estimating the true effect of the explanatory variables. In addition to addressing possible endogeneity issues associated with panel data, we include control variables at bank and country levels to suppress the possible effect of such characteristics. Random effect models have superiority in higher-level estimations. In order to select whether fixed or random

An extended model which comprises all the variables (including control

þ *β*3*Number of board meetingsi*,*<sup>t</sup>* þ *θ*1*Board diversityi*,*<sup>t</sup>* þ *θ*2*Non*

*CRi*,*<sup>t</sup>* ¼ *α* þ *β*1*Audit Committee expertisei*,*<sup>t</sup>* þ *β*2*Board policy functionsi*,*<sup>t</sup>*

effect models is suitable for estimation, we perform the Hausman test.

The results comprise descriptive statistics, correlation matrix and regression results for OLS, fixed effect, random effect and Hausman specification test.

**Table 1** shows the descriptive statistics (number of observations, means, standard deviation, minimum and maximum values) for the variables. The result shows high credit risk (mean = 0.948). Variables on board functions such as audit committee expertise (mean = 0.516) and board policy function (mean = 0.610) are average or just above average. This is a good sign for effective internal controls. The structure of the board shows good representation of females, more than average non-executive members and some banks having board chair who were former CEOs. Thus the banks have a balanced board which enables effective board activities. There is evidence of board monitoring and control as can be seen from audit committee independence and control activities. The mean risk-weighted assets

� *executive memberi*,*<sup>t</sup>* þ *θ*3*Chairman is ex* � *CEOi*,*<sup>t</sup>*

(2)

(3)

(4)

(6)

control variables.

*DOI: http://dx.doi.org/10.5772/intechopen.92889*

variables) follows:

þ *ε<sup>i</sup>*,*<sup>t</sup>*

**4. Results and discussion**

**25**

*H3: Higher risk weighted density increases bank credit risk. H4: Effective board monitoring reduces bank credit risk.*

#### **2.4 Control variables**

Bank management practices such as profitability motives affect the level of credit risk. Mixed findings exist in the relationship between credit risk and bank profitability. Studying the drivers of credit risk in the Indian banking industry, the authors find negative relation between ROA and credit risk [5]. The authors explain that banks engage in more prudent lending practices with improved borrower monitoring mechanisms to minimize the level of credit risk. Their findings confirm the work of Ghosh [39]. Others use credit risk as explanatory variable and conclude that there is a relation between credit risk and bank profitability in the US and Asia [26]. We argue that, the pursuit of profitability motives in the presence of weak internal control systems exacerbate bank credit risk exposure. In the model, we use return on assets (ROA) as proxy for profitability motives. The size of banks determines the volume of activities including loan portfolio which can determine credit risk. The BCBS internal control framework emphasizes coordinated effort between internal and external controls such as the regulator. Central banks use regulatory tools such as capital adequacy ratio and bank reserves to minimize bank risks.

#### **3. Data and methodology**

The initial sample comprises listed banks of countries within the European Union from Datastream and Worldscope databases. The websites of individuals and central banks of respective banks and countries provide further information about the banks under study. The period under study spans from 2004 to 2016. The chapter seeks to cover some period prior to the crisis, during and after the crisis. Based on data availability author analyzed the time period around the financial crisis, therefore some years before and some years after the crisis were taken into account. The databases compile bank-level data on corporate governance and financial performance variables. The analyses exclude banks with less than 5 years of data on the variables of interest. This makes the panel data unbalanced. Even though the data shows an initial 586 bank-year observations in the descriptive statistics, the regressions use 368 observations for the analyses.

#### **3.1 Internal controls variables**

The study classifies the 13 principles of the BCBS internal controls framework under three headings namely board activities and functions, board structure and board monitoring. The model is found below:

$$\text{CR}\_{i,t} = \alpha + \beta \sum\_{f=1}^{3} \text{BadFtn}\_{i,t} + \theta \sum\_{f=1}^{3} \text{BadFtn}\_{i,t} + \delta \sum\_{f=1}^{2} \text{BadMontit}\_{i,t} + \chi \text{Contr} + \varepsilon\_{i,t} \tag{1}$$

Where *CR* represents credit risk, *i* = 1 … ..56 banks, *t* = 2004 … 2016 *α* is the constant, *β*, *θ*, *δ*, *γ* are coefficients to be estimated and *ε* is the error term. *BodFtns* represents the set of variables on board activities and functions which affect credit risk, *BodStruc* represent the set of variables on board structure, *BodMonit* represent *Internal Controls and Credit Risk in European Banking: The Basel Committee on Banking… DOI: http://dx.doi.org/10.5772/intechopen.92889*

the set of variables on board monitoring activities and *Contr* represent the set of control variables.

*BodFtns* ¼ *f Audit Committee expertise* ð , *Board policy function*, *Number of board meetings*Þ (2)

*BodStruc* ¼ *f Board diversity* ð , *Non* � *executive members*,*Chairman is ex* � *CEO* (3)

*BodMonit* ¼ *f Audit committee independence* ð Þ , *Risk Weighted Assets to Total Assets*

(4)

*Contr* ¼ *f Capital adequacy ratio* ð Þ � *Tier* 1, *Bank profitability*, *Bank size* (5)

An extended model which comprises all the variables (including control variables) follows:

*CRi*,*<sup>t</sup>* ¼ *α* þ *β*1*Audit Committee expertisei*,*<sup>t</sup>* þ *β*2*Board policy functionsi*,*<sup>t</sup>* þ *β*3*Number of board meetingsi*,*<sup>t</sup>* þ *θ*1*Board diversityi*,*<sup>t</sup>* þ *θ*2*Non* � *executive memberi*,*<sup>t</sup>* þ *θ*3*Chairman is ex* � *CEOi*,*<sup>t</sup>* þ *δ*1*Audit committeee independencei*,*<sup>t</sup>* þ *δ*2*Risk weighted assets to total assetsi*,*<sup>t</sup>* þ *γ*1*Capital adequacy ratio* � *Tier* 1*<sup>i</sup>*,*<sup>t</sup>* þ *γ*2*BankSizei*,*<sup>t</sup>* þ *γ*3*Bank profitabilityi*,*<sup>t</sup>* þ *ε<sup>i</sup>*,*<sup>t</sup>*

(6)

The chapter uses the OLS, fixed effect and random effect estimation techniques. Sometimes, the assumptions of OLS may lead to biases and misleading standard errors hence the use of fixed and random effect models. The fixed effect model assumes that certain individual characteristics within may bias the model [40]. Torres-Reyna [40] explains this as the rationale behind the correlation between the error term and predictor variables. The fixed effect model removes the effect of time-invariant characteristics thereby perfectly estimating the true effect of the explanatory variables. In addition to addressing possible endogeneity issues associated with panel data, we include control variables at bank and country levels to suppress the possible effect of such characteristics. Random effect models have superiority in higher-level estimations. In order to select whether fixed or random effect models is suitable for estimation, we perform the Hausman test.

#### **4. Results and discussion**

The results comprise descriptive statistics, correlation matrix and regression results for OLS, fixed effect, random effect and Hausman specification test.

**Table 1** shows the descriptive statistics (number of observations, means, standard deviation, minimum and maximum values) for the variables. The result shows high credit risk (mean = 0.948). Variables on board functions such as audit committee expertise (mean = 0.516) and board policy function (mean = 0.610) are average or just above average. This is a good sign for effective internal controls. The structure of the board shows good representation of females, more than average non-executive members and some banks having board chair who were former CEOs. Thus the banks have a balanced board which enables effective board activities. There is evidence of board monitoring and control as can be seen from audit committee independence and control activities. The mean risk-weighted assets

(ratio or risk weighted assets to total assets) has a positive relation with bank credit

Bank management practices such as profitability motives affect the level of credit risk. Mixed findings exist in the relationship between credit risk and bank profitability. Studying the drivers of credit risk in the Indian banking industry, the authors find negative relation between ROA and credit risk [5]. The authors explain that banks engage in more prudent lending practices with improved borrower monitoring mechanisms to minimize the level of credit risk. Their findings confirm the work of Ghosh [39]. Others use credit risk as explanatory variable and conclude that there is a relation between credit risk and bank profitability in the US and Asia [26]. We argue that, the pursuit of profitability motives in the presence of weak internal control systems exacerbate bank credit risk exposure. In the model, we use return on assets (ROA) as proxy for profitability motives. The size of banks determines the volume of activities including loan portfolio which can determine credit risk. The BCBS internal control framework emphasizes coordinated effort between internal and external controls such as the regulator. Central banks use regulatory tools such as capital adequacy ratio and bank reserves to minimize bank risks.

The initial sample comprises listed banks of countries within the European Union from Datastream and Worldscope databases. The websites of individuals and central banks of respective banks and countries provide further information about the banks under study. The period under study spans from 2004 to 2016. The chapter seeks to cover some period prior to the crisis, during and after the crisis. Based on data availability author analyzed the time period around the financial crisis, therefore some years before and some years after the crisis were taken into account. The databases compile bank-level data on corporate governance and financial performance variables. The analyses exclude banks with less than 5 years of data on the variables of interest. This makes the panel data unbalanced. Even though the data shows an initial 586 bank-year observations in the descriptive

The study classifies the 13 principles of the BCBS internal controls framework under three headings namely board activities and functions, board structure and

*BodFtnsi*,*<sup>t</sup>* þ *δ*

Where *CR* represents credit risk, *i* = 1 … ..56 banks, *t* = 2004 … 2016 *α* is the constant, *β*, *θ*, *δ*, *γ* are coefficients to be estimated and *ε* is the error term. *BodFtns* represents the set of variables on board activities and functions which affect credit risk, *BodStruc* represent the set of variables on board structure, *BodMonit* represent

X 2

*BodMoniti*,*<sup>t</sup>* þ *γContr* þ *ε<sup>i</sup>*,*<sup>t</sup>* (1)

*J*¼1

statistics, the regressions use 368 observations for the analyses.

X 3

*J*¼1

risk [20]. From the discussions above, two hypotheses emerge: *H3: Higher risk weighted density increases bank credit risk. H4: Effective board monitoring reduces bank credit risk.*

**2.4 Control variables**

*Banking and Finance*

**3. Data and methodology**

**3.1 Internal controls variables**

X 3

*J*¼1

*CRi*,*<sup>t</sup>* ¼ *α* þ *β*

**24**

board monitoring. The model is found below:

*BodFtnsi*,*<sup>t</sup>* þ *θ*


#### **Table 1.**

*Descriptive statistics.*

(RWA) to total assets is almost 50%. This is an indication of high risk and it is therefore not surprising that credit risk is high among the banks sampled for the study.

**Table 2** shows the correlation matrix for the variables. High correlation coefficients (for example, 0.8 and above) are indications of high collinearity and this may cause problems in econometric estimations. The coefficients of the independent variables show that the variables are not highly correlated among themselves. This is an indication that the variables do not suffer multicollinearity problems.

We estimate the first model using OLS technique. Among board function variables, a number of board meetings show very significant relation with credit risk. Non-executive board members and chairman being ex-CEO are board structure variables that show significant relations with credit risk. Board activities (number of board meetings) shows significant positive relation with credit risk in the OLS model but the sign changes in the panel data analysis. Overall explanatory power of the OLS model is 26% which is far higher than those of fixed and random effects. Perhaps, this account for some of the biases of using OLS models instead of fixed and random effect models because OLS assumes that all the observations in the dataset are conditionally independent. This brings about bias and misleading standard errors. The study addresses bank heterogeneity using fixed and random effect models in a model that encompasses individual and time-specific effects. Based on the assumption that individual bank error term correlates with the predictor variables, we employ fixed effects model to cater for time-invariant omitted variables.

The difficulty in choosing fixed or random effect models is addressed by performing Hausman specification test. The significant variables for both fixed and random effect models are almost the same and consistent. The directions of association of the significant variables are the same. The use of Hausman specification test faces some criticisms. For example, when the between effect R2 is larger than the within effect R<sup>2</sup> , it is not appropriate to employ the fixed effect estimation technique even when Hausman test recommend the fixed effect model. The current study does not suffer such complications. In spite of the criticisms against the Hausman test, it is still widely used and accepted in research. The result from the Hausman test found in **Table 3** recommend the use of fixed effect model because

**Variables**

**27**

Net loans to

1.000

total loans Board policy

0.054

1.000

function

Numb of board

0.159

0.034

1.000

\*\*\*

meetings

RWA/Total

0.176

0.203

0.054

1.000

\*\*\*

\*\*\*

Assets

Audit com

0.208

0.223

0.086

 0.175

\*\*\*

1.000

\*\*\*

\*\*\*

independence

Audit com

0.041

 0.133

\*\*\*

0.039

0.089

0.090

1.000

*Internal Controls and Credit Risk in European Banking: The Basel Committee on Banking…*

*DOI: http://dx.doi.org/10.5772/intechopen.92889*

\*\*

\*\*

expertise

Non-exec board

Board diversity Chairman is ex-

0.064

 0.060

> CEO

Capital

0.003

0.032

0.033

0.187

0.169

0.239

0.010

 0.250

\*\*\*

0.179

1.000

\*\*\*

\*\*\*

\*\*\*

\*\*\*

adequacy ratio 1

ROA Bank size

*\**

*p < 0.1.*

*\*\*<sup>p</sup> < 0.05.*

*\*\*\*p < 0.01.*

**Table 2.** *Correlation*

 *matrix.*

0.052

 0.214\*\*\*

0.161

0.160

0.190

0.175

0.319

0.252

0.221

0.054

 0.567

\*\*\*

0.157

0.184

0.027 1.000

\*\*\*

\*\*\*

\*\*\*

\*\*\*

\*\*\*

\*\*\*

\*\*\*

0.081\*

0.083\*

0.041

 0.064

0.048

0.039

 0.132

\*\*\*

1.000

\*\*\*

\*\*\*

 0.039

 0.198

\*\*\*

0.058

0.062

0.042

 0.036

 0.042

 0.021

 0.095

\*\*

1.000

0.217

0.158

0.185

0.106

1.000

\*\*

\*\*\*

\*\*\*

\*\*\*

0.128

0.043

0.037

0.003

0.061

 0.036

 1.000

\*\*\*

 **Net loans to**

**Board policy**

**Number of board**

**RWA to**

**Audit**

**Audit comm**

**Non-exec.**

**Board**

**Chairman is**

**CAR-Tier 1**

**ROA Bank size**

**Board**

**diversity**

**ex-CEO**

**comm**

**expertis**

**indep**

**function**

**meetings**

**total assets**

**total loans**


*Internal Controls and Credit Risk in European Banking: The Basel Committee on Banking… DOI: http://dx.doi.org/10.5772/intechopen.92889*

> **Table 2.** *Correlation*

 *matrix.*

(RWA) to total assets is almost 50%. This is an indication of high risk and it is therefore not surprising that credit risk is high among the banks sampled for the

**Variable Obs Mean Std.Dev. Min Max** Net loans total loans 586 0.948 0.147 0 1.892 Audit committee independence 503 75.827 28.665 0 100 Audit committee expertise 586 51.62 30.667 3.46 89.04 Board policy functions 586 61.041 23.166 0.4 77.78 Board diversity 563 60.202 28.377 11.81 100 Non-executive members 532 66.63 19.392 3.99 100 Capital adequacy ratio-Tier 1 555 10.725 3.458 7.3 26.9 Chairman is ex-CEO 586 0.169 0.375 0 1 Number of board meetings 486 13.403 7.603 0 68 RWA to total assets 586 0.483 0.395 0 4.932 ROA 539 0.836 1.245 12.42 4.99 Bank size 586 19.44 1.479 16.624 22.579

**Table 2** shows the correlation matrix for the variables. High correlation coefficients (for example, 0.8 and above) are indications of high collinearity and this may cause problems in econometric estimations. The coefficients of the independent variables show that the variables are not highly correlated among themselves. This is an indication that the variables do not suffer multicollinearity

We estimate the first model using OLS technique. Among board function variables, a number of board meetings show very significant relation with credit risk. Non-executive board members and chairman being ex-CEO are board structure variables that show significant relations with credit risk. Board activities (number of board meetings) shows significant positive relation with credit risk in the OLS model but the sign changes in the panel data analysis. Overall explanatory power of the OLS model is 26% which is far higher than those of fixed and random effects. Perhaps, this account for some of the biases of using OLS models instead of fixed and random effect models because OLS assumes that all the observations in the dataset are conditionally independent. This brings about bias and misleading standard errors. The study addresses bank heterogeneity using fixed and random effect models in a model that encompasses individual and time-specific effects. Based on the assumption that individual bank error term correlates with the predictor variables, we employ fixed effects model to cater for time-invariant omitted variables. The difficulty in choosing fixed or random effect models is addressed by performing Hausman specification test. The significant variables for both fixed and random effect models are almost the same and consistent. The directions of association of the significant variables are the same. The use of Hausman specification test faces some criticisms. For example, when the between effect R2 is larger than the

, it is not appropriate to employ the fixed effect estimation tech-

nique even when Hausman test recommend the fixed effect model. The current study does not suffer such complications. In spite of the criticisms against the Hausman test, it is still widely used and accepted in research. The result from the Hausman test found in **Table 3** recommend the use of fixed effect model because

study.

**Table 1.**

*Descriptive statistics.*

*Banking and Finance*

problems.

within effect R<sup>2</sup>

**26**


*b = consistent under Ho and Ha; obtained from xtreg.*

*B = inconsistent under Ha, efficient under Ho; obtained from xtreg.*

*Test: Ho: difference in coefficients not systematic.*

*chi<sup>2</sup> (11) = (b B)'[(V\_b V\_B)^(1)](b B) = 7316.20*

*Prob>chi<sup>2</sup> = 0.0000*

*(V\_b V\_B is not positive definite)*

#### **Table 3.**

*Results for Hausman specification test.*

the probability Chi2 value is significant (p < 0.05). **Table 3** shows the results for Hausman test to choose whether fixed effects or random effects model is appropriate for estimation. When the p-value is significant (95% confidence interval), we reject the null hypothesis that random effect model is preferable. The result for the test shows high significance at 99% confidence interval. This implies the use of the fixed effect model for econometric estimation.

Apart from audit committee independence and board diversity, all the variables in model show some level of significance within 90–99% confidence interval. From **Table 4,** all the variables for board function show significant relation for the fixed and random effect models. This is unlike the OLS model which show significance for only number of board meetings. The fixed effect model caters for individual bank level biases that may influence credit risk. Even though all the countries are found within the European Union and may have some standardizations, there are still bank and country-specific factors which account for differences. This is why we assume that bank error terms do not correlate with the constant [40] thereby justifying the choice of fixed effects. The R2 results for within the entities are 41% for the fixed effect model. This indicates higher within entity variations. It is interesting to find that the R2 values for the fixed and random effect models are not different.

The expertise of the audit committee is within average which is a sign for good board function. However, this is unable to translate into credit risk mitigation. Contrary to the expectations that the expertise of the audit committee would minimize credit, the results indicate positive relation. The existence of quality audit committee does not guarantee effective risk reduction. Sun and Liu [41] caution that when members of the audit committee are too busy, the level of total and idiosyncratic risks is higher. Perhaps, members of the audit committee have a lot on their hands to deal with thereby making them less efficient in their functioning.

Board policy functions cover the development and implementation of internal controls, a culture of ethical behavior and compliance. The result shows high significant negative relation with credit risk. These conditions create a favorable environment for management oversight. Formulation and implementation of board policies ensure compliance with sound ethical behavior and enforcement of internal controls creates favorable environment to mitigate bank risks. The number of board meetings significantly reduces credit risk. It is not enough for board members to organize meetings but when members regularly attend and participate in board

**OLS (1) Fixed effects (2) Random effects (3)**

Net loans to total loans

Net loans to total loans

(0.000) (0.000) (0.000)

(0.000) (0.000) (0.000)

(0.001) (0.000) (0.000)

(0.000) (0.000) (0.000)

(0.000) (0.000) (0.000)

0.418\*\*\* 0.048\*\*\* 0.054\*\*\*

(0.052) (0.017) (0.017)

(0.017) (0.003) (0.003)

(0.000) (0.000) (0.000)

(0.002) (0.000) (0.000)

(0.006) (0.001) (0.001)

(0.007) (0.005) (0.004)

Net loans to total loans

Audit committee expertise 0.000 0.000\* 0.000\*

*Internal Controls and Credit Risk in European Banking: The Basel Committee on Banking…*

Board policy function 0.000 0.000\*\*\* 0.000\*\*\*

Number of board meetings 0.003\*\*\* 0.002\*\*\* 0.002\*\*\*

Non-executive board members 0.001\*\*\* 0.000\*\* 0.000\*\*\*

Audit committee independence 0.000 0.000 0.000

Chairman is ex-CEO 0.039\*\* 0.007\*\* 0.007\*\*

Board diversity 0.000 0.000 0.000

Capital adequacy ratio-Tier 1 0.005\*\* 0.001\*\* 0.001\*

ROA 0.004 0.004\*\*\* 0.004\*\*\*

Bank size 0.031\*\*\* 0.012\*\*\* 0.012\*\*\*

Obs. 368 368 368 R<sup>2</sup> within 0.408 0.408 R2 between 0.015 0.021 R2 overall 0.258 0.025 0.032

Risk weighted assets to total

*DOI: http://dx.doi.org/10.5772/intechopen.92889*

*Standard errors are in parenthesis.*

*Results for OLS, fixed and random effect models.*

*\*p < 0.1. \*\*p < 0.05. \*\*\*p < 0.01.*

**Table 4.**

**29**

assets


*Internal Controls and Credit Risk in European Banking: The Basel Committee on Banking… DOI: http://dx.doi.org/10.5772/intechopen.92889*

*\*\*\*p < 0.01.*

*\*\*p < 0.05.*

the probability Chi2 value is significant (p < 0.05). **Table 3** shows the results for

**Variables b B (b-B) sqrt (diag(V\_b-V\_B))**

Audit committee expertise 0.0000728 0.0000751 2.25e-06 **—** Board policy function 0.0004016 0.0003986 2.93e-06 **—** Number of board meetings 0.0018262 0.0017807 0.0000454 **—** Non-executive board members 0.0002097 0.0002187 9.04e-06 **—** Audit committee independence 4.89e-06 5.24e-06 0.0000101 **—** RWA/Total assets 0.0478272 0.0536965 0.0058693 **—** Chairman is ex-CEO 0.0074286 0.0072844 0.0001442 **—** Board diversity 0.0000737 0.0000732 5.15e-07 **—** Capital adequacy ratio-Tier 1 0.000748 0.0006744 0.0000736 **—** ROA 0.00425 0.0041675 0.0000825 **—** Bank size 0.0120867 0.0122416 0.0001548 0.0013089

**Fixed Random Difference SE**

Apart from audit committee independence and board diversity, all the variables in model show some level of significance within 90–99% confidence interval. From **Table 4,** all the variables for board function show significant relation for the fixed and random effect models. This is unlike the OLS model which show significance for only number of board meetings. The fixed effect model caters for individual bank level biases that may influence credit risk. Even though all the countries are found within the European Union and may have some standardizations, there are still bank and country-specific factors which account for differences. This is why we assume that bank error terms do not correlate with the constant [40] thereby justifying the choice of fixed effects. The R2 results for within the entities are 41% for the fixed effect model. This indicates higher within entity variations. It is interesting to find that the R2 values for the fixed and random effect models are not

The expertise of the audit committee is within average which is a sign for good board function. However, this is unable to translate into credit risk mitigation. Contrary to the expectations that the expertise of the audit committee would minimize credit, the results indicate positive relation. The existence of quality audit committee does not guarantee effective risk reduction. Sun and Liu [41] caution that when members of the audit committee are too busy, the level of total and idiosyncratic risks is higher. Perhaps, members of the audit committee have a lot on their hands to deal with thereby making them less efficient in their functioning.

Hausman test to choose whether fixed effects or random effects model is appropriate for estimation. When the p-value is significant (95% confidence interval), we reject the null hypothesis that random effect model is preferable. The result for the test shows high significance at 99% confidence interval. This implies

the use of the fixed effect model for econometric estimation.

*b = consistent under Ho and Ha; obtained from xtreg.*

*Test: Ho: difference in coefficients not systematic.*

*(V\_b V\_B is not positive definite)*

*Results for Hausman specification test.*

*chi<sup>2</sup>*

**Table 3.**

*Prob>chi<sup>2</sup> = 0.0000*

*Banking and Finance*

*B = inconsistent under Ha, efficient under Ho; obtained from xtreg.*

*(11) = (b B)'[(V\_b V\_B)^(1)](b B) = 7316.20*

different.

**28**

**Table 4.** *Results for OLS, fixed and random effect models.*

Board policy functions cover the development and implementation of internal controls, a culture of ethical behavior and compliance. The result shows high significant negative relation with credit risk. These conditions create a favorable environment for management oversight. Formulation and implementation of board policies ensure compliance with sound ethical behavior and enforcement of internal controls creates favorable environment to mitigate bank risks. The number of board meetings significantly reduces credit risk. It is not enough for board members to organize meetings but when members regularly attend and participate in board

activities. Regular board meetings improve the information and communication prowess of institutions which earns reputational capital [21, 22]. The result amplifies the institutional theory that normative elements and implementation of policies of acceptable behavior through compliance make institutions better governed. The development and implementation of board policies and engagement in board activities among sampled banks help reduce credit risk. Policies which ensure active participation of board activities, practicing a culture of ethical behavior and enforcement of internal control systems helps minimize bank losses. In this chapter, we find that board policies and board meetings have significant inverse relation with credit risk. Since two of the three variables adequately meet the expectation of the chapter, we maintain the acceptance of the hypothesis that board activities and functions minimize bank credit risk.

sampled banks, the result is not significant. Even though there is 75% score of audit committee independence among sampled banks, there is no evidence of significant relation with credit risk. It is good if variables show significant relation with outcome variables but when the direction of association is consistent with researcher's expectation, it is still worth reporting. The result cast doubts on the monitoring functions of the board and need to be given much attention than previously. The audit committee has consistently shown ineptitude in significantly minimizing credit risk among the sampled banks. From earlier result, the expertise of the audit committee could not significantly minimize credit risk and same has been reported

*Internal Controls and Credit Risk in European Banking: The Basel Committee on Banking…*

The results of the control variables meet the expectations of the authors. Bank profitability shows significant positive relation with credit risk. This means that the ambitious pursuit of profitability may lead to high credit risk and this is contradictory to earlier studies [5, 39]. The positive relation of bank profitability with credit is not surprising because of presence of board chair being ex-CEOs. There is the tendency for over-confidence and heavy reliance of experience to the neglect of strictly enforcing internal control mechanisms. The results from **Table 4** show that bank size significantly increases credit risk. Contrary to [5] who find no significant relation between bank size and credit risk, our study report significant positive relation with credit risk. For the purpose of catering for country-wide controls from external bodies such as regulators, the model introduces capital adequacy ratio-tier 1 into the equation. The results show significant negative relation with credit risk, thus confirming the effectiveness of regulatory controls to ensure bank compliance and discipline. Perhaps, banks have learnt lessons from the financial crisis. The result reinforces the institutional and agency theories used as the theoretical under-

Beside the Basel II framework which uses the Supervisory Review and Evaluation Process (SREP) to enforce capital requirements as risk management tool, banks are encouraged to develop and monitor other risk management techniques [42]. The use of the BCBS internal control framework through the governance systems complements the capital requirement models of bank risk management. The framework addresses issues of compliance, reporting and efficiency. The inclusion of capital adequacy ratio (also as a compliance mechanism) as proxy for regulatory control makes the chapter's conceptual model efficient in addressing credit risk in European banking. The results show that sampled banks invest in risky assets, have desire for profitability and therefore the board needs to intensify internal control

The study sought to analyses how board functions and activities, board structure and monitoring affect credit risk in European banking. Based on the BCBS internal control framework, we model the 13 principles of the BCBS framework under three headings namely board functions and activities, board structure and board monitoring. The results show that integrated internal control frameworks are complementary and proven to effectively mitigate bank credit risks. The study concludes that developing and implementing board policies on supervision, risk control culture, compliance and enforcement of internal controls minimizes credit risk in European banking. A board structure that ensures independence, diverse and board chair not being ex-CEO may reduce bank losses through credit risk. Board monitoring is effective when regulatory controls are used to complement existing internal control mechanisms. From the results, board policies, board activities, non-

on audit committee independence.

*DOI: http://dx.doi.org/10.5772/intechopen.92889*

pinnings of this study.

**5. Conclusion**

**31**

measures in order to minimize credit losses.

The results sustain the hypothesis that independent board structure reduces credit risk, whereas boards with weak independence increase credit risk. The variables for board structure for example non-executive board members show significant negative relation with credit risk. Non-executive board members have greater independence which makes them effective in their monitoring role. The inverse relation between non-executive board members and credit risk indicates effective control and prevention of actions that can trigger high credit risk. The result confirms the agency theory that non-executive board members help minimize the conflict of interest likely to exist. The positive relation between chair being ex-CEO and credit risk is not unexpected. Usually, such board members are influential and might exert superior powers which might increase bank credit risk. There is the tendency for over-confidence and unnecessarily entrenched leading to high credit risk. The result is consistent with Fernando et al. [25] who hold the opinion that dual board chair and CEO undermines board effectiveness in dealing with risks and monitoring managerialism. The BCBS internal control framework advocates for segregation of duties to ensure efficiency. It is not surprising the result shows positive relation between board chair being ex-CEO and credit risk. Board diversity (the proportion of female board members) shows negative but insignificant relation with credit risk. Even though not significant, board diversity is inversely related to credit risk. Having females on the board helps reduce credit risk. A board structure that compromises on its independence may have difficulty in effectively protecting and safeguarding the assets of shareholders. This assertion confirms earlier research by Karkowska and Acedański [9] that independent board structure decreases bank risks.

The chapter supports the hypothesis that higher risk weighted density increases bank credit risk. Board monitoring reduces credit risk of sampled banks. On the use of risk control mechanisms, risk-weighted assets to total assets shows significant positive relation with credit risk and therefore confirmatory to literature [20]. The mean RWA to total assets is almost 50% which is an indication of management investing in high risk investments. It is therefore not surprising that banks experienced high credit risk during the period under study. Relating the result to the control variable on bank profitability, the risk-return theory is confirmed. Banks engage in risky assets and this could explain why profitability (ROA) shows significant positive relation with credit risk. The European Union has experienced high non-performing loans (NPLs) during and after the global financial crisis, a situation which worsens banks credit portfolio performance. Bank control activities need to be intensified to check managerial recklessness in generating NPLs and subsequent credit risk which might lead to financial crisis.

The result for board monitoring shows that, audit committee independence reduces credit risk but not significantly. The hypothesis that board monitoring reduces bank credit risk is accepted in spite of the fact that in the case of the

#### *Internal Controls and Credit Risk in European Banking: The Basel Committee on Banking… DOI: http://dx.doi.org/10.5772/intechopen.92889*

sampled banks, the result is not significant. Even though there is 75% score of audit committee independence among sampled banks, there is no evidence of significant relation with credit risk. It is good if variables show significant relation with outcome variables but when the direction of association is consistent with researcher's expectation, it is still worth reporting. The result cast doubts on the monitoring functions of the board and need to be given much attention than previously. The audit committee has consistently shown ineptitude in significantly minimizing credit risk among the sampled banks. From earlier result, the expertise of the audit committee could not significantly minimize credit risk and same has been reported on audit committee independence.

The results of the control variables meet the expectations of the authors. Bank profitability shows significant positive relation with credit risk. This means that the ambitious pursuit of profitability may lead to high credit risk and this is contradictory to earlier studies [5, 39]. The positive relation of bank profitability with credit is not surprising because of presence of board chair being ex-CEOs. There is the tendency for over-confidence and heavy reliance of experience to the neglect of strictly enforcing internal control mechanisms. The results from **Table 4** show that bank size significantly increases credit risk. Contrary to [5] who find no significant relation between bank size and credit risk, our study report significant positive relation with credit risk. For the purpose of catering for country-wide controls from external bodies such as regulators, the model introduces capital adequacy ratio-tier 1 into the equation. The results show significant negative relation with credit risk, thus confirming the effectiveness of regulatory controls to ensure bank compliance and discipline. Perhaps, banks have learnt lessons from the financial crisis. The result reinforces the institutional and agency theories used as the theoretical underpinnings of this study.

Beside the Basel II framework which uses the Supervisory Review and Evaluation Process (SREP) to enforce capital requirements as risk management tool, banks are encouraged to develop and monitor other risk management techniques [42]. The use of the BCBS internal control framework through the governance systems complements the capital requirement models of bank risk management. The framework addresses issues of compliance, reporting and efficiency. The inclusion of capital adequacy ratio (also as a compliance mechanism) as proxy for regulatory control makes the chapter's conceptual model efficient in addressing credit risk in European banking. The results show that sampled banks invest in risky assets, have desire for profitability and therefore the board needs to intensify internal control measures in order to minimize credit losses.

#### **5. Conclusion**

activities. Regular board meetings improve the information and communication prowess of institutions which earns reputational capital [21, 22]. The result amplifies the institutional theory that normative elements and implementation of policies of acceptable behavior through compliance make institutions better governed. The development and implementation of board policies and engagement in board activities among sampled banks help reduce credit risk. Policies which ensure active participation of board activities, practicing a culture of ethical behavior and enforcement of internal control systems helps minimize bank losses. In this chapter, we find that board policies and board meetings have significant inverse relation with credit risk. Since two of the three variables adequately meet the expectation of the chapter, we maintain the acceptance of the hypothesis that board

The results sustain the hypothesis that independent board structure reduces credit risk, whereas boards with weak independence increase credit risk. The variables for board structure for example non-executive board members show significant negative relation with credit risk. Non-executive board members have greater independence which makes them effective in their monitoring role. The inverse relation between non-executive board members and credit risk indicates effective control and prevention of actions that can trigger high credit risk. The result confirms the agency theory that non-executive board members help minimize the conflict of interest likely to exist. The positive relation between chair being ex-CEO and credit risk is not unexpected. Usually, such board members are influential and might exert superior powers which might increase bank credit risk. There is the tendency for over-confidence and unnecessarily entrenched leading to high credit risk. The result is consistent with Fernando et al. [25] who hold the opinion that dual board chair and CEO undermines board effectiveness in dealing with risks and monitoring managerialism. The BCBS internal control framework advocates for segregation of duties to ensure efficiency. It is not surprising the result shows positive relation between board chair being ex-CEO and credit risk. Board diversity (the proportion of female board members) shows negative but insignificant relation with credit risk. Even though not significant, board diversity is inversely related to credit risk. Having females on the board helps reduce credit risk. A board structure that compromises on its independence may have difficulty in effectively protecting and safeguarding the assets of shareholders. This assertion confirms earlier research by Karkowska and Acedański [9] that independent

The chapter supports the hypothesis that higher risk weighted density increases bank credit risk. Board monitoring reduces credit risk of sampled banks. On the use of risk control mechanisms, risk-weighted assets to total assets shows significant positive relation with credit risk and therefore confirmatory to literature [20]. The mean RWA to total assets is almost 50% which is an indication of management investing in high risk investments. It is therefore not surprising that banks experienced high credit risk during the period under study. Relating the result to the control variable on bank profitability, the risk-return theory is confirmed. Banks engage in risky assets and this could explain why profitability (ROA) shows significant positive relation with credit risk. The European Union has experienced high non-performing loans (NPLs) during and after the global financial crisis, a situation which worsens banks credit portfolio performance. Bank control activities need to be intensified to check managerial recklessness in generating NPLs and subsequent

The result for board monitoring shows that, audit committee independence reduces credit risk but not significantly. The hypothesis that board monitoring reduces bank credit risk is accepted in spite of the fact that in the case of the

activities and functions minimize bank credit risk.

*Banking and Finance*

board structure decreases bank risks.

credit risk which might lead to financial crisis.

**30**

The study sought to analyses how board functions and activities, board structure and monitoring affect credit risk in European banking. Based on the BCBS internal control framework, we model the 13 principles of the BCBS framework under three headings namely board functions and activities, board structure and board monitoring. The results show that integrated internal control frameworks are complementary and proven to effectively mitigate bank credit risks. The study concludes that developing and implementing board policies on supervision, risk control culture, compliance and enforcement of internal controls minimizes credit risk in European banking. A board structure that ensures independence, diverse and board chair not being ex-CEO may reduce bank losses through credit risk. Board monitoring is effective when regulatory controls are used to complement existing internal control mechanisms. From the results, board policies, board activities, nonexecutive boards and external regulations significantly reduce credit risk. Whilst audit committee independence and board diversity reduce credit risk but not significantly, audit committee expertise, board chair being ex-CEO, investments in risky assets, profitability and bank size significantly increase credit risk. The model for the chapter shows that the principles of the BCBS framework combines with regulatory compliance requirements to ensure credit risk reduction. The chapter supports the agency and institutional theories. The BCBS internal control framework provides reliable mechanism for controlling credit risk.

**References**

[1] Basel Committee on Banking Supervision. Framework for Internal

[2] Kirkpatrick G. The corporate governance lessons from the financial crisis. OECD Journal: Financial Market Trends. 2009;**2009**(1):61–87. Available from: http://www.oecd-ilibrary.org/fina nce-and-investment/the-corporate-gove rnance-lessons-from-the-financial-c

risis\_fmt-v2009-art3-en

s11293-010-9240-4

574400\_EN.pdf

Organizations. Basel, Switzerland: Bank for International Settlements; 1998

*DOI: http://dx.doi.org/10.5772/intechopen.92889*

[8] Uhde DA, Klarner P, Tuschke A. Board monitoring of the chief financial officer: A review and research agenda. Corporate Governance: An International Review. 2017;**25**(2):116–133. DOI:

[9] Karkowska R, Acedański J. The effect of corporate board attributes on bank stability. Portuguese Economic Journal.

2019;**19**:99–137. DOI: 10.1007/

[10] Akbar S, Kharabsheh B, Poletti-Hughes J, Shah SZA. Board structure and corporate risk taking in the UK financial sector. International Review of Financial Analysis. 2017;**50**:101–110. DOI: 10.1016/j.irfa.2017.02.001

[11] Pathan S. Strong boards, CEO power and bank risk-taking. Journal of Bank ing and Finance. 2009;**33**(7):1340–1350. DOI: 10.1016/jbankfin.2009.02.001

[12] John K, De Masi S, Paci A. Corporate

[13] Gualandri E. Basel 3, pillar 2: The role of banks' internal governance and control function. SSRN Electronic Journal. 2011:1–12. DOI: 10.2139/

[14] International Federation of Accountants. Internal Controls—A Review of Current Developments. 2006 (August). New York. pp.1-19. Available from: http://www.ifac.org/sites/default/

files/publications/files/internal-

[15] Hansen J, Stephens NM, Wood DA. Entity-level controls: The internal auditor's assessment of management tone at the top. Current Issues in Auditing. 2009;**3**(1):1–13. DOI: 10.2308/

controls-a-revie.pdf

ciia.2009.3.1.A1

governance in banks. Corporate Governance: An International Review. 2016;**24**(3):303–321. DOI: 10.1111/

corg.12161

ssrn.1908641

10.1111/corg.12188

*Internal Controls and Credit Risk in European Banking: The Basel Committee on Banking…*

s10258-019.00162-3

[3] Lang WW, Jagtiani JA. The mortgage and financial crises: The role of credit risk management and corporate

governance. Atlantic Economic Journal. 2010;**38**(3):295–316. DOI: 10.1007/

[4] Mesnard B, Margerit A, Power C, Magnus M. Non-performing loans in the

Banking Union: Stocktaking and challenges. In: European Parliament, Economic Governance and Support Unit. 2016. Available from: http://www. europarl.europa.eu/RegData/etudes/ BRIE/2016/574400/IPOL\_BRI(2016)

[5] Gulati R, Goswami A, Kumar S. What drives credit risk in the Indian banking industry? An empirical

**43**(1):42–62. DOI: 10.1016/j.

ecosys.2018.08.004

10.3926/ic.703

**33**

investigation. Economic Systems. 2019;

[6] Akwaa-Sekyi EK, Moreno JG. Effect of internal controls on credit risk among listed Spanish banks. Intangible Capital. 2016;**12**(1):357–389. DOI:

[7] Cho M, Chung K-H. The effect of commercial banks' internal control weaknesses on loan loss reserves and provisions. Journal of Contemporary Accounting and Economics. 2016;**12**(1): 61–72. DOI: 10.1016/j.jcae.2016.02.004

Control Systems in Banking

The study has implications for bank practice. Credit risk continues to be a thorny issue in the banking industry especially within the EU. Our study provides a diversified approach to addressing this market failure. The chapter shows that complementing regulatory controls with self-governing practices like internal controls reduce bank risks. This research is not devoid of limitations. But for the availability of data, the study could have substantially covered three periods (before, during and after the crisis). Despite these limitations, the methodology is consistent with existing research and all assumptions and diagnostic tests were statistically confirmed. These limitations cast no doubts about the findings of our study. The chapter suggests future research to consider internal control practices in the periods before, during and after the 2007 global financial crisis. It is further suggested that, various internal control frameworks could be compared to analyze their effects on other risks such as market, liquidity and operational risks. Future research could also consider using dynamic models such as system GMM to study corporate governance and bank risks.
